{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-11-02</td>\n",
       "      <td>778.200012</td>\n",
       "      <td>781.650024</td>\n",
       "      <td>763.450012</td>\n",
       "      <td>768.700012</td>\n",
       "      <td>768.700012</td>\n",
       "      <td>1872400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-11-03</td>\n",
       "      <td>767.250000</td>\n",
       "      <td>769.950012</td>\n",
       "      <td>759.030029</td>\n",
       "      <td>762.130005</td>\n",
       "      <td>762.130005</td>\n",
       "      <td>1943200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-11-04</td>\n",
       "      <td>750.659973</td>\n",
       "      <td>770.359985</td>\n",
       "      <td>750.560974</td>\n",
       "      <td>762.020020</td>\n",
       "      <td>762.020020</td>\n",
       "      <td>2134800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>774.500000</td>\n",
       "      <td>785.190002</td>\n",
       "      <td>772.549988</td>\n",
       "      <td>782.520020</td>\n",
       "      <td>782.520020</td>\n",
       "      <td>1585100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>783.400024</td>\n",
       "      <td>795.632996</td>\n",
       "      <td>780.190002</td>\n",
       "      <td>790.510010</td>\n",
       "      <td>790.510010</td>\n",
       "      <td>1350800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Open        High         Low       Close   Adj Close  \\\n",
       "0  2016-11-02  778.200012  781.650024  763.450012  768.700012  768.700012   \n",
       "1  2016-11-03  767.250000  769.950012  759.030029  762.130005  762.130005   \n",
       "2  2016-11-04  750.659973  770.359985  750.560974  762.020020  762.020020   \n",
       "3  2016-11-07  774.500000  785.190002  772.549988  782.520020  782.520020   \n",
       "4  2016-11-08  783.400024  795.632996  780.190002  790.510010  790.510010   \n",
       "\n",
       "    Volume  \n",
       "0  1872400  \n",
       "1  1943200  \n",
       "2  2134800  \n",
       "3  1585100  \n",
       "4  1350800  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filepath = 'D:/Jupyter/Project01/dataset/rlData.csv'\n",
    "data = pd.read_csv(filepath)\n",
    "data = data.sort_values('Date')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(252, 7)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style(\"darkgrid\")\n",
    "plt.figure(figsize = (15,9))\n",
    "plt.plot(data[['Close']])\n",
    "plt.xticks(range(0,data.shape[0],20), data['Date'].loc[::20], rotation=45)\n",
    "plt.title(\"****** Stock Price\",fontsize=18, fontweight='bold')\n",
    "plt.xlabel('Date',fontsize=18)\n",
    "plt.ylabel('Close Price (USD)',fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 252 entries, 0 to 251\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Close   252 non-null    float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 3.9 KB\n"
     ]
    }
   ],
   "source": [
    "price = data[['Close']]\n",
    "price.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\application\\Anaconda3\\envs\\torch\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "price['Close'] = scaler.fit_transform(price['Close'].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(252,)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price['Close'].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 数据集制作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_data(stock, lookback):\n",
    "    data_raw = stock.to_numpy() \n",
    "    data = []\n",
    "    \n",
    "    # you can free play（seq_length）\n",
    "    for index in range(len(data_raw) - lookback): \n",
    "        data.append(data_raw[index: index + lookback])\n",
    "    \n",
    "    data = np.array(data);\n",
    "    test_set_size = int(np.round(0.2 * data.shape[0]))\n",
    "    train_set_size = data.shape[0] - (test_set_size)\n",
    "    \n",
    "    x_train = data[:train_set_size,:-1,:]\n",
    "    y_train = data[:train_set_size,-1,:]\n",
    "    \n",
    "    x_test = data[train_set_size:,:-1]\n",
    "    y_test = data[train_set_size:,-1,:]\n",
    "    \n",
    "    return [x_train, y_train, x_test, y_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train.shape =  (186, 19, 1)\n",
      "y_train.shape =  (186, 1)\n",
      "x_test.shape =  (46, 19, 1)\n",
      "y_test.shape =  (46, 1)\n"
     ]
    }
   ],
   "source": [
    "lookback = 20\n",
    "x_train, y_train, x_test, y_test = split_data(price, lookback)\n",
    "print('x_train.shape = ',x_train.shape)\n",
    "print('y_train.shape = ',y_train.shape)\n",
    "print('x_test.shape = ',x_test.shape)\n",
    "print('y_test.shape = ',y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意：pytorch的nn.LSTM input shape=(seq_length, batch_size, input_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 模型构建 —— LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "x_train = torch.from_numpy(x_train).type(torch.Tensor)\n",
    "x_test = torch.from_numpy(x_test).type(torch.Tensor)\n",
    "y_train_lstm = torch.from_numpy(y_train).type(torch.Tensor)\n",
    "y_test_lstm = torch.from_numpy(y_test).type(torch.Tensor)\n",
    "y_train_gru = torch.from_numpy(y_train).type(torch.Tensor)\n",
    "y_test_gru = torch.from_numpy(y_test).type(torch.Tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_dim = 1\n",
    "hidden_dim = 32\n",
    "num_layers = 2\n",
    "output_dim = 1\n",
    "num_epochs = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LSTM(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim, num_layers, output_dim):\n",
    "        super(LSTM, self).__init__()\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.num_layers = num_layers\n",
    "        \n",
    "        self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()\n",
    "        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()\n",
    "        out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))\n",
    "        out = self.fc(out[:, -1, :]) \n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LSTM(input_dim=input_dim, hidden_dim=hidden_dim, output_dim=output_dim, num_layers=num_layers)\n",
    "criterion = torch.nn.MSELoss()\n",
    "optimiser = torch.optim.Adam(model.parameters(), lr=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  0 MSE:  0.203866645693779\n",
      "Epoch  1 MSE:  0.18269051611423492\n",
      "Epoch  2 MSE:  0.14787845313549042\n",
      "Epoch  3 MSE:  0.08377688378095627\n",
      "Epoch  4 MSE:  0.02260478399693966\n",
      "Epoch  5 MSE:  0.1297089010477066\n",
      "Epoch  6 MSE:  0.038854312151670456\n",
      "Epoch  7 MSE:  0.016073033213615417\n",
      "Epoch  8 MSE:  0.032561589032411575\n",
      "Epoch  9 MSE:  0.04485313594341278\n",
      "Epoch  10 MSE:  0.04685841128230095\n",
      "Epoch  11 MSE:  0.04175002500414848\n",
      "Epoch  12 MSE:  0.03448764234781265\n",
      "Epoch  13 MSE:  0.028441516682505608\n",
      "Epoch  14 MSE:  0.02597387507557869\n",
      "Epoch  15 MSE:  0.026360314339399338\n",
      "Epoch  16 MSE:  0.02638128399848938\n",
      "Epoch  17 MSE:  0.023632245138287544\n",
      "Epoch  18 MSE:  0.019355041906237602\n",
      "Epoch  19 MSE:  0.015260438434779644\n",
      "Epoch  20 MSE:  0.013051943853497505\n",
      "Epoch  21 MSE:  0.012425185181200504\n",
      "Epoch  22 MSE:  0.012803087010979652\n",
      "Epoch  23 MSE:  0.013621477410197258\n",
      "Epoch  24 MSE:  0.014309358783066273\n",
      "Epoch  25 MSE:  0.014758080244064331\n",
      "Epoch  26 MSE:  0.01493602804839611\n",
      "Epoch  27 MSE:  0.014761639758944511\n",
      "Epoch  28 MSE:  0.014352821744978428\n",
      "Epoch  29 MSE:  0.01393033191561699\n",
      "Epoch  30 MSE:  0.013532127253711224\n",
      "Epoch  31 MSE:  0.013078879565000534\n",
      "Epoch  32 MSE:  0.012575330212712288\n",
      "Epoch  33 MSE:  0.012048502452671528\n",
      "Epoch  34 MSE:  0.011475919745862484\n",
      "Epoch  35 MSE:  0.01092013344168663\n",
      "Epoch  36 MSE:  0.010486445389688015\n",
      "Epoch  37 MSE:  0.010159676894545555\n",
      "Epoch  38 MSE:  0.009904453530907631\n",
      "Epoch  39 MSE:  0.009733145125210285\n",
      "Epoch  40 MSE:  0.00959516130387783\n",
      "Epoch  41 MSE:  0.00945411529392004\n",
      "Epoch  42 MSE:  0.009350068867206573\n",
      "Epoch  43 MSE:  0.009265526197850704\n",
      "Epoch  44 MSE:  0.009188761934638023\n",
      "Epoch  45 MSE:  0.009139510802924633\n",
      "Epoch  46 MSE:  0.009069452062249184\n",
      "Epoch  47 MSE:  0.0089749526232481\n",
      "Epoch  48 MSE:  0.008861863054335117\n",
      "Epoch  49 MSE:  0.008715507574379444\n",
      "Epoch  50 MSE:  0.008572452701628208\n",
      "Epoch  51 MSE:  0.008429903537034988\n",
      "Epoch  52 MSE:  0.008286502212285995\n",
      "Epoch  53 MSE:  0.008150729350745678\n",
      "Epoch  54 MSE:  0.00800260715186596\n",
      "Epoch  55 MSE:  0.007852857932448387\n",
      "Epoch  56 MSE:  0.00770726939663291\n",
      "Epoch  57 MSE:  0.007566670887172222\n",
      "Epoch  58 MSE:  0.0074433558620512486\n",
      "Epoch  59 MSE:  0.0073247915133833885\n",
      "Epoch  60 MSE:  0.0072079305537045\n",
      "Epoch  61 MSE:  0.007092184387147427\n",
      "Epoch  62 MSE:  0.006974120624363422\n",
      "Epoch  63 MSE:  0.0068647186271846294\n",
      "Epoch  64 MSE:  0.006760487798601389\n",
      "Epoch  65 MSE:  0.006658927537500858\n",
      "Epoch  66 MSE:  0.006556554231792688\n",
      "Epoch  67 MSE:  0.006448805332183838\n",
      "Epoch  68 MSE:  0.006343234796077013\n",
      "Epoch  69 MSE:  0.006239596754312515\n",
      "Epoch  70 MSE:  0.006140170618891716\n",
      "Epoch  71 MSE:  0.006041037384420633\n",
      "Epoch  72 MSE:  0.005941199138760567\n",
      "Epoch  73 MSE:  0.005845363717526197\n",
      "Epoch  74 MSE:  0.005755205638706684\n",
      "Epoch  75 MSE:  0.005671427119523287\n",
      "Epoch  76 MSE:  0.005588183179497719\n",
      "Epoch  77 MSE:  0.005508118309080601\n",
      "Epoch  78 MSE:  0.0054332236759364605\n",
      "Epoch  79 MSE:  0.005365678109228611\n",
      "Epoch  80 MSE:  0.0052990783005952835\n",
      "Epoch  81 MSE:  0.005233069881796837\n",
      "Epoch  82 MSE:  0.005168124567717314\n",
      "Epoch  83 MSE:  0.0051041520200669765\n",
      "Epoch  84 MSE:  0.0050370595417916775\n",
      "Epoch  85 MSE:  0.00496941152960062\n",
      "Epoch  86 MSE:  0.004902905784547329\n",
      "Epoch  87 MSE:  0.004837151616811752\n",
      "Epoch  88 MSE:  0.004771598149091005\n",
      "Epoch  89 MSE:  0.004709118977189064\n",
      "Epoch  90 MSE:  0.004650257993489504\n",
      "Epoch  91 MSE:  0.004593721125274897\n",
      "Epoch  92 MSE:  0.004540140274912119\n",
      "Epoch  93 MSE:  0.004490258637815714\n",
      "Epoch  94 MSE:  0.004443286452442408\n",
      "Epoch  95 MSE:  0.004397979471832514\n",
      "Epoch  96 MSE:  0.00435525132343173\n",
      "Epoch  97 MSE:  0.0043147653341293335\n",
      "Epoch  98 MSE:  0.004275809042155743\n",
      "Epoch  99 MSE:  0.004238244611769915\n",
      "Training time: 5.726648807525635\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "hist = np.zeros(num_epochs)\n",
    "start_time = time.time()\n",
    "lstm = []\n",
    "\n",
    "for t in range(num_epochs):\n",
    "    y_train_pred = model(x_train)\n",
    "\n",
    "    loss = criterion(y_train_pred, y_train_lstm)\n",
    "    print(\"Epoch \", t, \"MSE: \", loss.item())\n",
    "    hist[t] = loss.item()\n",
    "\n",
    "    optimiser.zero_grad()\n",
    "    loss.backward()\n",
    "    optimiser.step()\n",
    "    \n",
    "training_time = time.time()-start_time\n",
    "print(\"Training time: {}\".format(training_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. 模型结果可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = pd.DataFrame(scaler.inverse_transform(y_train_pred.detach().numpy()))\n",
    "original = pd.DataFrame(scaler.inverse_transform(y_train_lstm.detach().numpy()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_style(\"darkgrid\")    \n",
    "\n",
    "fig = plt.figure()\n",
    "fig.subplots_adjust(hspace=0.2, wspace=0.2)\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "ax = sns.lineplot(x = original.index, y = original[0], label=\"Data\", color='royalblue')\n",
    "ax = sns.lineplot(x = predict.index, y = predict[0], label=\"Training Prediction (LSTM)\", color='tomato')\n",
    "ax.set_title('Stock price', size = 14, fontweight='bold')\n",
    "ax.set_xlabel(\"Days\", size = 14)\n",
    "ax.set_ylabel(\"Cost (USD)\", size = 14)\n",
    "ax.set_xticklabels('', size=10)\n",
    "\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "ax = sns.lineplot(data=hist, color='royalblue')\n",
    "ax.set_xlabel(\"Epoch\", size = 14)\n",
    "ax.set_ylabel(\"Loss\", size = 14)\n",
    "ax.set_title(\"Training Loss\", size = 14, fontweight='bold')\n",
    "fig.set_figheight(6)\n",
    "fig.set_figwidth(16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. 模型验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.4899],\n",
       "        [0.6162],\n",
       "        [0.6779],\n",
       "        [0.6648],\n",
       "        [0.6344],\n",
       "        [0.7495],\n",
       "        [0.7397],\n",
       "        [0.7525],\n",
       "        [0.7685],\n",
       "        [0.7697],\n",
       "        [0.7741],\n",
       "        [0.7163],\n",
       "        [0.7422],\n",
       "        [0.6058],\n",
       "        [0.6202],\n",
       "        [0.6395],\n",
       "        [0.6342],\n",
       "        [0.9569],\n",
       "        [0.9420]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 9.42 RMSE\n",
      "Test Score: 14.61 RMSE\n"
     ]
    }
   ],
   "source": [
    "import math, time\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# make predictions\n",
    "y_test_pred = model(x_test)\n",
    "\n",
    "# invert predictions\n",
    "y_train_pred = scaler.inverse_transform(y_train_pred.detach().numpy())\n",
    "y_train = scaler.inverse_transform(y_train_lstm.detach().numpy())\n",
    "y_test_pred = scaler.inverse_transform(y_test_pred.detach().numpy())\n",
    "y_test = scaler.inverse_transform(y_test_lstm.detach().numpy())\n",
    "\n",
    "# calculate root mean squared error\n",
    "trainScore = math.sqrt(mean_squared_error(y_train[:,0], y_train_pred[:,0]))\n",
    "print('Train Score: %.2f RMSE' % (trainScore))\n",
    "testScore = math.sqrt(mean_squared_error(y_test[:,0], y_test_pred[:,0]))\n",
    "print('Test Score: %.2f RMSE' % (testScore))\n",
    "lstm.append(trainScore)\n",
    "lstm.append(testScore)\n",
    "lstm.append(training_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shift train predictions for plotting\n",
    "trainPredictPlot = np.empty_like(price)\n",
    "trainPredictPlot[:, :] = np.nan\n",
    "trainPredictPlot[lookback:len(y_train_pred)+lookback, :] = y_train_pred\n",
    "\n",
    "# shift test predictions for plotting\n",
    "testPredictPlot = np.empty_like(price)\n",
    "testPredictPlot[:, :] = np.nan\n",
    "testPredictPlot[len(y_train_pred)+lookback-1:len(price)-1, :] = y_test_pred\n",
    "\n",
    "original = scaler.inverse_transform(price['Close'].values.reshape(-1,1))\n",
    "\n",
    "predictions = np.append(trainPredictPlot, testPredictPlot, axis=1)\n",
    "predictions = np.append(predictions, original, axis=1)\n",
    "result = pd.DataFrame(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plotly.com"
       },
       "data": [
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       "                            \n",
       "var gd = document.getElementById('2bba6531-7c6a-4ca3-aeb0-3188f018cf86');\n",
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       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(go.Scatter(x=result.index, y=result[0],\n",
    "                    mode='lines',\n",
    "                    name='Train prediction')))\n",
    "fig.add_trace(go.Scatter(x=result.index, y=result[1],\n",
    "                    mode='lines',\n",
    "                    name='Test prediction'))\n",
    "fig.add_trace(go.Scatter(go.Scatter(x=result.index, y=result[2],\n",
    "                    mode='lines',\n",
    "                    name='Actual Value')))\n",
    "fig.update_layout(\n",
    "    xaxis=dict(\n",
    "        showline=True,\n",
    "        showgrid=True,\n",
    "        showticklabels=False,\n",
    "        linecolor='white',\n",
    "        linewidth=2\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title_text='Close (USD)',\n",
    "        titlefont=dict(\n",
    "            family='Rockwell',\n",
    "            size=12,\n",
    "            color='white',\n",
    "        ),\n",
    "        showline=True,\n",
    "        showgrid=True,\n",
    "        showticklabels=True,\n",
    "        linecolor='white',\n",
    "        linewidth=2,\n",
    "        ticks='outside',\n",
    "        tickfont=dict(\n",
    "            family='Rockwell',\n",
    "            size=12,\n",
    "            color='white',\n",
    "        ),\n",
    "    ),\n",
    "    showlegend=True,\n",
    "    template = 'plotly_dark'\n",
    "\n",
    ")\n",
    "\n",
    "\n",
    "\n",
    "annotations = []\n",
    "annotations.append(dict(xref='paper', yref='paper', x=0.0, y=1.05,\n",
    "                              xanchor='left', yanchor='bottom',\n",
    "                              text='Results (LSTM)',\n",
    "                              font=dict(family='Rockwell',\n",
    "                                        size=26,\n",
    "                                        color='white'),\n",
    "                              showarrow=False))\n",
    "fig.update_layout(annotations=annotations)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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